# t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification

arXiv: https://arxiv.org/abs/1804.09618 · HTML: https://mlsanity.com/paper/1804.09618/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 19 of 19)

- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [Does Audio Deepfake Detection Generalize?](https://mlsanity.com/paper/2203.16263/) (2022)
- [ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan](https://mlsanity.com/paper/2109.00535/) (2021)
- [SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing](https://mlsanity.com/paper/2211.02718/) (2022)
- [Deepfake audio detection by speaker verification](https://mlsanity.com/paper/2209.14098/) (2022)
- [Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection](https://mlsanity.com/paper/2104.03123/) (2021)
- [Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck](https://mlsanity.com/paper/2204.01387/) (2022)
- [Retrieval-Augmented Audio Deepfake Detection](https://mlsanity.com/paper/2404.13892/) (2024)
- [Range-Based Equal Error Rate for Spoof Localization](https://mlsanity.com/paper/2305.17739/) (2023)
- [Targeted Augmented Data for Audio Deepfake Detection](https://mlsanity.com/paper/2407.07598/) (2024)
- [Optimizing Tandem Speaker Verification and Anti-Spoofing Systems](https://mlsanity.com/paper/2201.09709/) (2022)
- [ConvNeXt Based Neural Network for Audio Anti-Spoofing](https://mlsanity.com/paper/2209.06434/) (2022)
- [Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion](https://mlsanity.com/paper/2305.16353/) (2023)
- [Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples](https://mlsanity.com/paper/2408.13341/) (2024)
- [FADEL: Uncertainty-aware Fake Audio Detection with Evidential Deep Learning](https://mlsanity.com/paper/2504.15663/) (2025)
- [Complementing Handcrafted Features with Raw Waveform Using a Light-weight Auxiliary Model](https://mlsanity.com/paper/2109.02773/) (2021)
- [Towards the Development of a Real-Time Deepfake Audio Detection System in Communication Platforms](https://mlsanity.com/paper/2403.11778/) (2024)
- [On Evaluating Quantum Kernel Robustness for Low-Resource Cross-Corpus Audio Deepfake Detection](https://mlsanity.com/paper/2610.00649/) (2026)

## Abstract (arXiv)

The ASVspoof challenge series was born to spearhead research in anti-spoofing for automatic speaker verification (ASV). The two challenge editions in 2015 and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation from ASV using an equal error rate (EER) metric. While a strategic approach to assessment at the time, it has certain shortcomings. First, the CM EER is not necessarily a reliable predictor of performance when ASV and CMs are combined. Second, the EER operating point is ill-suited to user authentication applications, e.g. telephone banking, characterised by a high target user prior but a low spoofing attack prior. We aim to migrate from CM- to ASV-centric assessment with the aid of a new tandem detection cost function (t-DCF) metric. It extends the conventional DCF used in ASV research to scenarios involving spoofing attacks. The t-DCF metric has 6 parameters: (i) false alarm and miss costs for both systems, and (ii) prior probabilities of target and spoof trials (with an implied third, nontarget prior). The study is intended to serve as a self-contained, tutorial-like presentation. We analyse with the t-DCF a selection of top-performing CM submissions to the 2015 and 2017 editions of ASVspoof, with a focus on the spoofing attack prior. Whereas there is little to choose between countermeasure systems for lower priors, system rankings derived with the EER and t-DCF show differences for higher priors. We observe some ranking changes. Findings support the adoption of the DCF-based metric into the roadmap for future ASVspoof challenges, and possibly for other biometric anti-spoofing evaluations.